{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "20d1844d-56a1-4d97-9098-04cd38614402",
   "metadata": {},
   "source": [
    "# Seasonal dummy features to capture seasonality"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c7e22b9-366f-4754-ba9c-c642f5b6f291",
   "metadata": {},
   "source": [
    "[Feature Engineering for Time Series Forecasting](https://www.trainindata.com/p/feature-engineering-for-forecasting)\n",
    "\n",
    "In this notebook we will show how to create seasonal dummy features and how we can add them to our forecasting pipeline. Seasonal dummy features are helpful to use with linear models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9a86e7d9-212c-4d82-b896-20a4e078c336",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import re\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_context(\"talk\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abfa9133-5964-4bc0-99f7-7fc976548995",
   "metadata": {},
   "source": [
    "# Data set synopsis\n",
    "\n",
    "We will use the Victoria electricity demand dataset found here: \n",
    "https://github.com/tidyverts/tsibbledata/tree/master/data-raw/vic_elec. This dataset is used in the [original MSTL paper [1]](https://arxiv.org/pdf/2107.13462.pdf). It is the total electricity demand at a half hourly granularity for the state of Victora in Australia from 2002 to the start of 2015. A more detailed description of the dataset can be found [here](https://rdrr.io/cran/tsibbledata/man/vic_elec.html). \n",
    "\n",
    "We resampled the dataset to hourly in the 4th data preparation notebook in the \"01-Create-Datasets\" folder in this repo. For instructions on how to download, prepare, and store the dataset, refer to notebook number 4, in the folder \"01-Create-Datasets\" from this repo.\n",
    "\n",
    "## References\n",
    "[1] [K. Bandura, R.J. Hyndman, and C. Bergmeir (2021)\n",
    "    MSTL: A Seasonal-Trend Decomposition Algorithm for Time Series with Multiple\n",
    "    Seasonal Patterns. arXiv preprint arXiv:2107.13462.](https://arxiv.org/pdf/2107.13462.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51692532-584b-4244-844d-3aab67b14f99",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9a8af020-f4e4-4bce-8dea-e4e7d57819a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\n",
    "    \"../Datasets/victoria_electricity_demand.csv\",\n",
    "    usecols=[\"demand\", \"date_time\"],\n",
    "    parse_dates=[\"date_time\"],\n",
    "    index_col=[\"date_time\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9c075934-e6ed-4ab4-9596-c40d233328a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(115368, 1)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa5c67f5-a2ff-4dc4-9105-53f9e4ca54ac",
   "metadata": {},
   "source": [
    "The data is relatively large compared to the other datasets we have been working with. If the rest of this notebook runs too slowly on your laptop try filtering to a recent segment of the data. For example by running:\n",
    "\n",
    "```Python\n",
    "# Filter to the previous 3 years of the dataset\n",
    "data = data.loc[\"2012\":]\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea3cc441-df77-439b-a88d-e74c334635c3",
   "metadata": {},
   "source": [
    "## Plot the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3f51b1e-88b1-4be2-a68b-5484f19e72aa",
   "metadata": {},
   "source": [
    "There time series is high frequency and over a long period. Let's plot the previous three years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f7d59f9a-ace8-44ba-ad6e-7a49013d4937",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=[20, 5])\n",
    "data.loc[\"2012\":].plot(y=\"demand\", legend=None, ax=ax)\n",
    "ax.set_xlabel(\"Time\")\n",
    "ax.set_ylabel(\"Electricity Demand\")\n",
    "ax.set_title(\"Electricity Demand\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2683607-c1ab-4b60-b700-05ae7d36f105",
   "metadata": {},
   "source": [
    "# Using sklearn to create dummy features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7356493d-69fe-4a29-aa81-aca085b87382",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "# Let's ensure all sklearn transformers output pandas dataframes\n",
    "from sklearn import set_config\n",
    "set_config(transform_output=\"pandas\")  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "828ed0fd-c988-4d85-9dd5-fbca3c1d074e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = data.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cee1e049-e2ba-44a8-a5fa-3e60b9593f9e",
   "metadata": {},
   "source": [
    "Let's create some features from the date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a83d7426-63e1-44a2-8de1-010495156399",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"month_of_year\"] = df.index.month\n",
    "df[\"week_of_year\"] = df.index.isocalendar().week"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56174822-c69e-4e46-8b97-ce6054f208f4",
   "metadata": {},
   "source": [
    "Create the one hot encoder transformer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "602619d8-3a6f-4fca-8d6f-799bf6f57065",
   "metadata": {},
   "outputs": [],
   "source": [
    "transformer = OneHotEncoder(sparse_output=False, # Required to enable\n",
    "                                                 # pandas output.\n",
    "                            drop=\"first\", # To avoid the dummy variable\n",
    "                                          # trap we drop the first dummy.\n",
    "                            )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28985e23-8a7d-4ef3-a4d6-850e44ff93e0",
   "metadata": {},
   "source": [
    "Create seasonal dummy variables from our date features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a0d4c9ea-a0de-44b1-9461-a04a1c120007",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month_of_year_2</th>\n",
       "      <th>month_of_year_3</th>\n",
       "      <th>month_of_year_4</th>\n",
       "      <th>month_of_year_5</th>\n",
       "      <th>month_of_year_6</th>\n",
       "      <th>month_of_year_7</th>\n",
       "      <th>month_of_year_8</th>\n",
       "      <th>month_of_year_9</th>\n",
       "      <th>month_of_year_10</th>\n",
       "      <th>month_of_year_11</th>\n",
       "      <th>...</th>\n",
       "      <th>week_of_year_44.0</th>\n",
       "      <th>week_of_year_45.0</th>\n",
       "      <th>week_of_year_46.0</th>\n",
       "      <th>week_of_year_47.0</th>\n",
       "      <th>week_of_year_48.0</th>\n",
       "      <th>week_of_year_49.0</th>\n",
       "      <th>week_of_year_50.0</th>\n",
       "      <th>week_of_year_51.0</th>\n",
       "      <th>week_of_year_52.0</th>\n",
       "      <th>week_of_year_53.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2002-01-01 00:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01 01:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01 02:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01 03:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01 04:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     month_of_year_2  month_of_year_3  month_of_year_4  \\\n",
       "date_time                                                                \n",
       "2002-01-01 00:00:00              0.0              0.0              0.0   \n",
       "2002-01-01 01:00:00              0.0              0.0              0.0   \n",
       "2002-01-01 02:00:00              0.0              0.0              0.0   \n",
       "2002-01-01 03:00:00              0.0              0.0              0.0   \n",
       "2002-01-01 04:00:00              0.0              0.0              0.0   \n",
       "\n",
       "                     month_of_year_5  month_of_year_6  month_of_year_7  \\\n",
       "date_time                                                                \n",
       "2002-01-01 00:00:00              0.0              0.0              0.0   \n",
       "2002-01-01 01:00:00              0.0              0.0              0.0   \n",
       "2002-01-01 02:00:00              0.0              0.0              0.0   \n",
       "2002-01-01 03:00:00              0.0              0.0              0.0   \n",
       "2002-01-01 04:00:00              0.0              0.0              0.0   \n",
       "\n",
       "                     month_of_year_8  month_of_year_9  month_of_year_10  \\\n",
       "date_time                                                                 \n",
       "2002-01-01 00:00:00              0.0              0.0               0.0   \n",
       "2002-01-01 01:00:00              0.0              0.0               0.0   \n",
       "2002-01-01 02:00:00              0.0              0.0               0.0   \n",
       "2002-01-01 03:00:00              0.0              0.0               0.0   \n",
       "2002-01-01 04:00:00              0.0              0.0               0.0   \n",
       "\n",
       "                     month_of_year_11  ...  week_of_year_44.0  \\\n",
       "date_time                              ...                      \n",
       "2002-01-01 00:00:00               0.0  ...                0.0   \n",
       "2002-01-01 01:00:00               0.0  ...                0.0   \n",
       "2002-01-01 02:00:00               0.0  ...                0.0   \n",
       "2002-01-01 03:00:00               0.0  ...                0.0   \n",
       "2002-01-01 04:00:00               0.0  ...                0.0   \n",
       "\n",
       "                     week_of_year_45.0  week_of_year_46.0  week_of_year_47.0  \\\n",
       "date_time                                                                      \n",
       "2002-01-01 00:00:00                0.0                0.0                0.0   \n",
       "2002-01-01 01:00:00                0.0                0.0                0.0   \n",
       "2002-01-01 02:00:00                0.0                0.0                0.0   \n",
       "2002-01-01 03:00:00                0.0                0.0                0.0   \n",
       "2002-01-01 04:00:00                0.0                0.0                0.0   \n",
       "\n",
       "                     week_of_year_48.0  week_of_year_49.0  week_of_year_50.0  \\\n",
       "date_time                                                                      \n",
       "2002-01-01 00:00:00                0.0                0.0                0.0   \n",
       "2002-01-01 01:00:00                0.0                0.0                0.0   \n",
       "2002-01-01 02:00:00                0.0                0.0                0.0   \n",
       "2002-01-01 03:00:00                0.0                0.0                0.0   \n",
       "2002-01-01 04:00:00                0.0                0.0                0.0   \n",
       "\n",
       "                     week_of_year_51.0  week_of_year_52.0  week_of_year_53.0  \n",
       "date_time                                                                     \n",
       "2002-01-01 00:00:00                0.0                0.0                0.0  \n",
       "2002-01-01 01:00:00                0.0                0.0                0.0  \n",
       "2002-01-01 02:00:00                0.0                0.0                0.0  \n",
       "2002-01-01 03:00:00                0.0                0.0                0.0  \n",
       "2002-01-01 04:00:00                0.0                0.0                0.0  \n",
       "\n",
       "[5 rows x 63 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = transformer.fit_transform(df[[\"month_of_year\", \"week_of_year\"]])\n",
    "result.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afed5ad0-f353-4ee5-86f9-c82bd453b2e4",
   "metadata": {},
   "source": [
    "# Creating seasonal dummies using sklearn and sktime"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "614f20e0-9061-4628-957c-f58b29d6b9ad",
   "metadata": {},
   "source": [
    "Let's combine the `DateTimeFeatures` transformer from sktime with `OneHotEncoder` inside of a pipeline to make it easier to create seasonal dummy features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2cf14b06-1206-4217-9ba2-559739d130b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.transformations.series.date import DateTimeFeatures\n",
    "from sklearn.pipeline import make_pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c82f5c06-fb56-4a0e-ab0f-03335eccd0a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;datetimefeatures&#x27;,\n",
       "                 DateTimeFeatures(manual_selection=[&#x27;week_of_year&#x27;,\n",
       "                                                    &#x27;month_of_year&#x27;])),\n",
       "                (&#x27;onehotencoder&#x27;,\n",
       "                 OneHotEncoder(drop=&#x27;first&#x27;, sparse_output=False))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;datetimefeatures&#x27;,\n",
       "                 DateTimeFeatures(manual_selection=[&#x27;week_of_year&#x27;,\n",
       "                                                    &#x27;month_of_year&#x27;])),\n",
       "                (&#x27;onehotencoder&#x27;,\n",
       "                 OneHotEncoder(drop=&#x27;first&#x27;, sparse_output=False))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DateTimeFeatures</label><div class=\"sk-toggleable__content\"><pre>DateTimeFeatures(manual_selection=[&#x27;week_of_year&#x27;, &#x27;month_of_year&#x27;])</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, sparse_output=False)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('datetimefeatures',\n",
       "                 DateTimeFeatures(manual_selection=['week_of_year',\n",
       "                                                    'month_of_year'])),\n",
       "                ('onehotencoder',\n",
       "                 OneHotEncoder(drop='first', sparse_output=False))])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Specify which datetime features to create \n",
    "# and then convert into dummy/one hot features.\n",
    "seasonal_dummies = [\n",
    "                    \"week_of_year\",\n",
    "                    \"month_of_year\",\n",
    "                   ]\n",
    "\n",
    "\n",
    "# Create the DateTimeFeatures transformer\n",
    "datetime_transformer = DateTimeFeatures(manual_selection=seasonal_dummies, # Select which features to\n",
    "                                                                           # create\n",
    "                                       keep_original_columns=False, # Flag if we want to keep columns\n",
    "                                                                    # in dataframe passed to `transform`.\n",
    "                                      )\n",
    "\n",
    "# One hot encoder\n",
    "one_hot_encoder = OneHotEncoder(sparse_output=False, # Required to enable\n",
    "                                                     # pandas output.\n",
    "                                drop=\"first\"\n",
    "                               )\n",
    "\n",
    "# Combine the two transformers in a pipeline\n",
    "seasonal_dummies_feats = make_pipeline(datetime_transformer, one_hot_encoder)\n",
    "seasonal_dummies_feats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "932c40bd-3dc1-45a0-b196-e680c7b4fe0d",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>month_of_year_2</th>\n",
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       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01 04:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     month_of_year_2  month_of_year_3  month_of_year_4  \\\n",
       "date_time                                                                \n",
       "2002-01-01 00:00:00              0.0              0.0              0.0   \n",
       "2002-01-01 01:00:00              0.0              0.0              0.0   \n",
       "2002-01-01 02:00:00              0.0              0.0              0.0   \n",
       "2002-01-01 03:00:00              0.0              0.0              0.0   \n",
       "2002-01-01 04:00:00              0.0              0.0              0.0   \n",
       "\n",
       "                     month_of_year_5  month_of_year_6  month_of_year_7  \\\n",
       "date_time                                                                \n",
       "2002-01-01 00:00:00              0.0              0.0              0.0   \n",
       "2002-01-01 01:00:00              0.0              0.0              0.0   \n",
       "2002-01-01 02:00:00              0.0              0.0              0.0   \n",
       "2002-01-01 03:00:00              0.0              0.0              0.0   \n",
       "2002-01-01 04:00:00              0.0              0.0              0.0   \n",
       "\n",
       "                     month_of_year_8  month_of_year_9  month_of_year_10  \\\n",
       "date_time                                                                 \n",
       "2002-01-01 00:00:00              0.0              0.0               0.0   \n",
       "2002-01-01 01:00:00              0.0              0.0               0.0   \n",
       "2002-01-01 02:00:00              0.0              0.0               0.0   \n",
       "2002-01-01 03:00:00              0.0              0.0               0.0   \n",
       "2002-01-01 04:00:00              0.0              0.0               0.0   \n",
       "\n",
       "                     month_of_year_11  ...  week_of_year_44  week_of_year_45  \\\n",
       "date_time                              ...                                     \n",
       "2002-01-01 00:00:00               0.0  ...              0.0              0.0   \n",
       "2002-01-01 01:00:00               0.0  ...              0.0              0.0   \n",
       "2002-01-01 02:00:00               0.0  ...              0.0              0.0   \n",
       "2002-01-01 03:00:00               0.0  ...              0.0              0.0   \n",
       "2002-01-01 04:00:00               0.0  ...              0.0              0.0   \n",
       "\n",
       "                     week_of_year_46  week_of_year_47  week_of_year_48  \\\n",
       "date_time                                                                \n",
       "2002-01-01 00:00:00              0.0              0.0              0.0   \n",
       "2002-01-01 01:00:00              0.0              0.0              0.0   \n",
       "2002-01-01 02:00:00              0.0              0.0              0.0   \n",
       "2002-01-01 03:00:00              0.0              0.0              0.0   \n",
       "2002-01-01 04:00:00              0.0              0.0              0.0   \n",
       "\n",
       "                     week_of_year_49  week_of_year_50  week_of_year_51  \\\n",
       "date_time                                                                \n",
       "2002-01-01 00:00:00              0.0              0.0              0.0   \n",
       "2002-01-01 01:00:00              0.0              0.0              0.0   \n",
       "2002-01-01 02:00:00              0.0              0.0              0.0   \n",
       "2002-01-01 03:00:00              0.0              0.0              0.0   \n",
       "2002-01-01 04:00:00              0.0              0.0              0.0   \n",
       "\n",
       "                     week_of_year_52  week_of_year_53  \n",
       "date_time                                              \n",
       "2002-01-01 00:00:00              0.0              0.0  \n",
       "2002-01-01 01:00:00              0.0              0.0  \n",
       "2002-01-01 02:00:00              0.0              0.0  \n",
       "2002-01-01 03:00:00              0.0              0.0  \n",
       "2002-01-01 04:00:00              0.0              0.0  \n",
       "\n",
       "[5 rows x 63 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit and transform to create our features\n",
    "result = seasonal_dummies_feats.fit_transform(data)\n",
    "result.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d20fba2-1d7b-472c-a73a-d67e55173bb7",
   "metadata": {},
   "source": [
    "# Let's build some forecasts!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e835157-d0e9-4571-81dc-6fa818ffb032",
   "metadata": {},
   "source": [
    "Let's build a recursive forecast and see how our seasonal dummy features can help capture seasonality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "738606c1-df44-4f9f-b4d7-4e5a1f22d234",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Require OneHotEncoder to create seasonal dummy variables\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "# --- The transformers from earlier in the course. --- #\n",
    "# Date time features to capture seasonality\n",
    "from sktime.transformations.series.date import DateTimeFeatures\n",
    "# Lag and window features\n",
    "from sktime.transformations.series.summarize import WindowSummarizer\n",
    "# Time features for trend \n",
    "from sktime.transformations.series.time_since import TimeSince\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "# Rescaling transformer for linear models with regularisation\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "# Pipelines to create feature engineering pipeline\n",
    "from sklearn.pipeline import make_pipeline, make_union\n",
    "# Used to reset sklearn estimators\n",
    "from sklearn.base import clone\n",
    "\n",
    "# Let's ensure all sklearn transformers output pandas dataframes\n",
    "from sklearn import set_config\n",
    "set_config(transform_output=\"pandas\")  # Upgrade to scikit-learn 0.12\n",
    "                                       # for this feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "130d5c18-2870-4a1a-89af-c09bdff6b775",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>demand</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2002-01-01 00:00:00</th>\n",
       "      <td>6919.366092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01 01:00:00</th>\n",
       "      <td>7165.974188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01 02:00:00</th>\n",
       "      <td>6406.542994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01 03:00:00</th>\n",
       "      <td>5815.537828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01 04:00:00</th>\n",
       "      <td>5497.732922</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          demand\n",
       "date_time                       \n",
       "2002-01-01 00:00:00  6919.366092\n",
       "2002-01-01 01:00:00  7165.974188\n",
       "2002-01-01 02:00:00  6406.542994\n",
       "2002-01-01 03:00:00  5815.537828\n",
       "2002-01-01 04:00:00  5497.732922"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = data.copy()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d292e0e-e5ce-4c5d-b63a-6490a6457b6b",
   "metadata": {},
   "source": [
    "Specify target name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "25d2262f-c242-409b-93b4-0913f70c3113",
   "metadata": {},
   "outputs": [],
   "source": [
    "target=[\"demand\"] # Note: it's in a list.\n",
    "                  # This ensures we'll get\n",
    "                  # a dataframe when using df.loc[:, target]\n",
    "                  # rather than a pandas Series. \n",
    "                  # This can also be useful if we have\n",
    "                  # multiple targets."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dac1165-9d17-4648-b7ed-927ed660de3d",
   "metadata": {},
   "source": [
    "Prepare our transformers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "002c3527-412a-4dc4-b359-1c829a8a4789",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Polynomial time features for trend\n",
    "time_feats = make_pipeline(\n",
    "                           TimeSince(), \n",
    "                           PolynomialFeatures(degree=1, include_bias=False)\n",
    "                          )\n",
    "\n",
    "# Create the seasonal dummies transformer.\n",
    "# Specify which datetime features to create\n",
    "# dummies with.\n",
    "datetime_features = [\n",
    "                       \"month_of_year\",\n",
    "                       \"day_of_week\",\n",
    "                       \"hour_of_day\",\n",
    "                    ]\n",
    "\n",
    "datetime_feats = DateTimeFeatures(manual_selection=datetime_features, \n",
    "                               keep_original_columns=False, \n",
    "                              )\n",
    "one_hot_encoder = OneHotEncoder(sparse_output=False, # Required to enable\n",
    "                                                     # pandas output.\n",
    "                                drop=\"first\"\n",
    "                               )\n",
    "seasonal_dummies_feats = make_pipeline(datetime_feats, one_hot_encoder)\n",
    "\n",
    "\n",
    "# Features computed from the target.\n",
    "# Compute lag and window features.\n",
    "lag_window_feats = WindowSummarizer(\n",
    "    lag_feature={\n",
    "        \"lag\": [1]  # Lag features.\n",
    "    },\n",
    "    target_cols=target,\n",
    "    truncate=\"bfill\",  # Backfill missing values from lagging and windowing.\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d0525e4-b9a3-4977-88e6-cc213e4d8a9e",
   "metadata": {},
   "source": [
    "Create a pipeline to create all our features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8224a415-81da-4471-9e65-53f0ed4b7ed4",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = make_union(\n",
    "    seasonal_dummies_feats,\n",
    "    time_feats, \n",
    "    lag_window_feats,\n",
    ")\n",
    "\n",
    "# Apply min-max scaling to all the features\n",
    "pipeline = make_pipeline(pipeline, MinMaxScaler())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b74e1d09-c547-42dc-b736-f3e55d756749",
   "metadata": {},
   "outputs": [
    {
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       "                 FeatureUnion(transformer_list=[(&#x27;pipeline-1&#x27;,\n",
       "                                                 Pipeline(steps=[(&#x27;datetimefeatures&#x27;,\n",
       "                                                                  DateTimeFeatures(manual_selection=[&#x27;month_of_year&#x27;,\n",
       "                                                                                                     &#x27;day_of_week&#x27;,\n",
       "                                                                                                     &#x27;hour_of_day&#x27;])),\n",
       "                                                                 (&#x27;onehotencoder&#x27;,\n",
       "                                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                sparse_output=False))])),\n",
       "                                                (&#x27;pipeline-2&#x27;,\n",
       "                                                 Pipeline(steps=[(&#x27;timesince&#x27;,\n",
       "                                                                  TimeSince()),\n",
       "                                                                 (&#x27;polynomialfeatures&#x27;,\n",
       "                                                                  PolynomialFeatures(degree=1,\n",
       "                                                                                     include_bias=False))])),\n",
       "                                                (&#x27;windowsummarizer&#x27;,\n",
       "                                                 WindowSummarizer(lag_feature={&#x27;lag&#x27;: [1]},\n",
       "                                                                  target_cols=[&#x27;demand&#x27;],\n",
       "                                                                  truncate=&#x27;bfill&#x27;))])),\n",
       "                (&#x27;minmaxscaler&#x27;, MinMaxScaler())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;featureunion&#x27;,\n",
       "                 FeatureUnion(transformer_list=[(&#x27;pipeline-1&#x27;,\n",
       "                                                 Pipeline(steps=[(&#x27;datetimefeatures&#x27;,\n",
       "                                                                  DateTimeFeatures(manual_selection=[&#x27;month_of_year&#x27;,\n",
       "                                                                                                     &#x27;day_of_week&#x27;,\n",
       "                                                                                                     &#x27;hour_of_day&#x27;])),\n",
       "                                                                 (&#x27;onehotencoder&#x27;,\n",
       "                                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                sparse_output=False))])),\n",
       "                                                (&#x27;pipeline-2&#x27;,\n",
       "                                                 Pipeline(steps=[(&#x27;timesince&#x27;,\n",
       "                                                                  TimeSince()),\n",
       "                                                                 (&#x27;polynomialfeatures&#x27;,\n",
       "                                                                  PolynomialFeatures(degree=1,\n",
       "                                                                                     include_bias=False))])),\n",
       "                                                (&#x27;windowsummarizer&#x27;,\n",
       "                                                 WindowSummarizer(lag_feature={&#x27;lag&#x27;: [1]},\n",
       "                                                                  target_cols=[&#x27;demand&#x27;],\n",
       "                                                                  truncate=&#x27;bfill&#x27;))])),\n",
       "                (&#x27;minmaxscaler&#x27;, MinMaxScaler())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">featureunion: FeatureUnion</label><div class=\"sk-toggleable__content\"><pre>FeatureUnion(transformer_list=[(&#x27;pipeline-1&#x27;,\n",
       "                                Pipeline(steps=[(&#x27;datetimefeatures&#x27;,\n",
       "                                                 DateTimeFeatures(manual_selection=[&#x27;month_of_year&#x27;,\n",
       "                                                                                    &#x27;day_of_week&#x27;,\n",
       "                                                                                    &#x27;hour_of_day&#x27;])),\n",
       "                                                (&#x27;onehotencoder&#x27;,\n",
       "                                                 OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                               sparse_output=False))])),\n",
       "                               (&#x27;pipeline-2&#x27;,\n",
       "                                Pipeline(steps=[(&#x27;timesince&#x27;, TimeSince()),\n",
       "                                                (&#x27;polynomialfeatures&#x27;,\n",
       "                                                 PolynomialFeatures(degree=1,\n",
       "                                                                    include_bias=False))])),\n",
       "                               (&#x27;windowsummarizer&#x27;,\n",
       "                                WindowSummarizer(lag_feature={&#x27;lag&#x27;: [1]},\n",
       "                                                 target_cols=[&#x27;demand&#x27;],\n",
       "                                                 truncate=&#x27;bfill&#x27;))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>pipeline-1</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DateTimeFeatures</label><div class=\"sk-toggleable__content\"><pre>DateTimeFeatures(manual_selection=[&#x27;month_of_year&#x27;, &#x27;day_of_week&#x27;,\n",
       "                                   &#x27;hour_of_day&#x27;])</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, sparse_output=False)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>pipeline-2</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TimeSince</label><div class=\"sk-toggleable__content\"><pre>TimeSince()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PolynomialFeatures</label><div class=\"sk-toggleable__content\"><pre>PolynomialFeatures(degree=1, include_bias=False)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>windowsummarizer</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">WindowSummarizer</label><div class=\"sk-toggleable__content\"><pre>WindowSummarizer(lag_feature={&#x27;lag&#x27;: [1]}, target_cols=[&#x27;demand&#x27;],\n",
       "                 truncate=&#x27;bfill&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('featureunion',\n",
       "                 FeatureUnion(transformer_list=[('pipeline-1',\n",
       "                                                 Pipeline(steps=[('datetimefeatures',\n",
       "                                                                  DateTimeFeatures(manual_selection=['month_of_year',\n",
       "                                                                                                     'day_of_week',\n",
       "                                                                                                     'hour_of_day'])),\n",
       "                                                                 ('onehotencoder',\n",
       "                                                                  OneHotEncoder(drop='first',\n",
       "                                                                                sparse_output=False))])),\n",
       "                                                ('pipeline-2',\n",
       "                                                 Pipeline(steps=[('timesince',\n",
       "                                                                  TimeSince()),\n",
       "                                                                 ('polynomialfeatures',\n",
       "                                                                  PolynomialFeatures(degree=1,\n",
       "                                                                                     include_bias=False))])),\n",
       "                                                ('windowsummarizer',\n",
       "                                                 WindowSummarizer(lag_feature={'lag': [1]},\n",
       "                                                                  target_cols=['demand'],\n",
       "                                                                  truncate='bfill'))])),\n",
       "                ('minmaxscaler', MinMaxScaler())])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6decf02c-1a0a-45d0-9fec-07255f232a17",
   "metadata": {},
   "source": [
    "Let's check how our feature engineering pipeline behaves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8071a5af-cfb4-4a2b-85d8-d0e065f53fa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month_of_year_2</th>\n",
       "      <th>month_of_year_3</th>\n",
       "      <th>month_of_year_4</th>\n",
       "      <th>month_of_year_5</th>\n",
       "      <th>month_of_year_6</th>\n",
       "      <th>month_of_year_7</th>\n",
       "      <th>month_of_year_8</th>\n",
       "      <th>month_of_year_9</th>\n",
       "      <th>month_of_year_10</th>\n",
       "      <th>month_of_year_11</th>\n",
       "      <th>...</th>\n",
       "      <th>hour_of_day_16</th>\n",
       "      <th>hour_of_day_17</th>\n",
       "      <th>hour_of_day_18</th>\n",
       "      <th>hour_of_day_19</th>\n",
       "      <th>hour_of_day_20</th>\n",
       "      <th>hour_of_day_21</th>\n",
       "      <th>hour_of_day_22</th>\n",
       "      <th>hour_of_day_23</th>\n",
       "      <th>time_since_2002-01-01 00:00:00</th>\n",
       "      <th>demand_lag_1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2002-01-01 00:00:00</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.112939</td>\n",
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       "    <tr>\n",
       "      <th>2002-01-01 01:00:00</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.112939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01 02:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.131101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01 03:00:00</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.000026</td>\n",
       "      <td>0.075171</td>\n",
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       "    <tr>\n",
       "      <th>2002-01-01 04:00:00</th>\n",
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       "      <td>0.000035</td>\n",
       "      <td>0.031645</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     month_of_year_2  month_of_year_3  month_of_year_4  \\\n",
       "date_time                                                                \n",
       "2002-01-01 00:00:00              0.0              0.0              0.0   \n",
       "2002-01-01 01:00:00              0.0              0.0              0.0   \n",
       "2002-01-01 02:00:00              0.0              0.0              0.0   \n",
       "2002-01-01 03:00:00              0.0              0.0              0.0   \n",
       "2002-01-01 04:00:00              0.0              0.0              0.0   \n",
       "\n",
       "                     month_of_year_5  month_of_year_6  month_of_year_7  \\\n",
       "date_time                                                                \n",
       "2002-01-01 00:00:00              0.0              0.0              0.0   \n",
       "2002-01-01 01:00:00              0.0              0.0              0.0   \n",
       "2002-01-01 02:00:00              0.0              0.0              0.0   \n",
       "2002-01-01 03:00:00              0.0              0.0              0.0   \n",
       "2002-01-01 04:00:00              0.0              0.0              0.0   \n",
       "\n",
       "                     month_of_year_8  month_of_year_9  month_of_year_10  \\\n",
       "date_time                                                                 \n",
       "2002-01-01 00:00:00              0.0              0.0               0.0   \n",
       "2002-01-01 01:00:00              0.0              0.0               0.0   \n",
       "2002-01-01 02:00:00              0.0              0.0               0.0   \n",
       "2002-01-01 03:00:00              0.0              0.0               0.0   \n",
       "2002-01-01 04:00:00              0.0              0.0               0.0   \n",
       "\n",
       "                     month_of_year_11  ...  hour_of_day_16  hour_of_day_17  \\\n",
       "date_time                              ...                                   \n",
       "2002-01-01 00:00:00               0.0  ...             0.0             0.0   \n",
       "2002-01-01 01:00:00               0.0  ...             0.0             0.0   \n",
       "2002-01-01 02:00:00               0.0  ...             0.0             0.0   \n",
       "2002-01-01 03:00:00               0.0  ...             0.0             0.0   \n",
       "2002-01-01 04:00:00               0.0  ...             0.0             0.0   \n",
       "\n",
       "                     hour_of_day_18  hour_of_day_19  hour_of_day_20  \\\n",
       "date_time                                                             \n",
       "2002-01-01 00:00:00             0.0             0.0             0.0   \n",
       "2002-01-01 01:00:00             0.0             0.0             0.0   \n",
       "2002-01-01 02:00:00             0.0             0.0             0.0   \n",
       "2002-01-01 03:00:00             0.0             0.0             0.0   \n",
       "2002-01-01 04:00:00             0.0             0.0             0.0   \n",
       "\n",
       "                     hour_of_day_21  hour_of_day_22  hour_of_day_23  \\\n",
       "date_time                                                             \n",
       "2002-01-01 00:00:00             0.0             0.0             0.0   \n",
       "2002-01-01 01:00:00             0.0             0.0             0.0   \n",
       "2002-01-01 02:00:00             0.0             0.0             0.0   \n",
       "2002-01-01 03:00:00             0.0             0.0             0.0   \n",
       "2002-01-01 04:00:00             0.0             0.0             0.0   \n",
       "\n",
       "                     time_since_2002-01-01 00:00:00  demand_lag_1  \n",
       "date_time                                                          \n",
       "2002-01-01 00:00:00                        0.000000      0.112939  \n",
       "2002-01-01 01:00:00                        0.000009      0.112939  \n",
       "2002-01-01 02:00:00                        0.000017      0.131101  \n",
       "2002-01-01 03:00:00                        0.000026      0.075171  \n",
       "2002-01-01 04:00:00                        0.000035      0.031645  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit_transform(df).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faf8a062-c2be-4d22-9b1f-73be7f52b4f0",
   "metadata": {},
   "source": [
    "Let's reset our feature engineering pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "22ad95c0-b937-471c-a2ae-fe17ed2d3f2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can use `clone` to return an unfitted version\n",
    "# of the pipeline.\n",
    "pipeline = clone(pipeline)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9445dbf-0c67-4e52-9df7-ffc5ff178ef6",
   "metadata": {},
   "source": [
    "Let's build a recursive forecast."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c79eb0e-d6e1-40eb-a930-80900822f66f",
   "metadata": {},
   "source": [
    "We'll start with configuring the model, the forecast start time, the number of steps to forecast, and the forecasting horizon, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3e812426-089d-4add-bbb7-8a630e879efd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fee3871f-b8ca-4063-9272-37c0f71f626a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- CONFIG --- #\n",
    "# Define time of first forecast, this determines our train / test split.                                              \n",
    "forecast_start_time = df.index.max() - pd.DateOffset(weeks=2) # Start two weeks from the end.\n",
    "\n",
    "# Define number of steps to forecast.\n",
    "num_of_forecast_steps = 24*14 # 2 weeks, this is a lot! Reduce this\n",
    "                              # if you want the recursive forecast\n",
    "                              # to run more quickly. \n",
    "\n",
    "# Define the model.\n",
    "model = LinearRegression()\n",
    "\n",
    "# Create a list of periods that we'll forecast over.\n",
    "forecast_horizon = pd.date_range(forecast_start_time, \n",
    "                                   periods=num_of_forecast_steps,\n",
    "                                   freq=\"H\")\n",
    "\n",
    "# How much data in the past is needed to create our features\n",
    "look_back_window_size = pd.DateOffset(weeks=1) # We need the latest 24*7 time periods\n",
    "                                             # in our predict dataframe to build our \n",
    "                                             # window features."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ffaef60-a54d-4d0c-a19b-e4e5202ec896",
   "metadata": {},
   "source": [
    "Let's create our training dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e25af278-6269-4c5f-8c8c-7ff0cd1c01fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- CREATE TRAINING & TESTING DATAFRAME  --- #\n",
    "# Ensure we only have training data up to the start\n",
    "# of the forecast.\n",
    "df_train = df.loc[df.index < forecast_start_time].copy()\n",
    "df_test = df.loc[df.index >= forecast_start_time].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2000b65-2517-417d-82c7-7c8860a1c646",
   "metadata": {},
   "source": [
    "Let's compute our `X_train` and `y_train` and fit our model!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "528ab868-ea31-40cf-aafb-410e29fe9eb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# --- FEATURE ENGINEERING--- #\n",
    "# Create X_train and y_train\n",
    "y_train = df_train[target]\n",
    "X_train = pipeline.fit_transform(df_train)\n",
    "\n",
    "# LightGBM cannot handle column names which have\n",
    "# certain characters (e.g., \":\"). We replace these\n",
    "# with `_`. \n",
    "if \"lightgbm\" in model.__module__: # checks if model is from lightgbm\n",
    "    X_train = X_train.rename(columns = lambda x:re.sub(\"[^A-Za-z0-9_]+\", \"_\", x))\n",
    "\n",
    "\n",
    "# --- MODEL TRAINING---#\n",
    "# Train one-step ahead forecast model\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1deb325e-4226-49bc-a826-a0c34024e47c",
   "metadata": {},
   "source": [
    "Let's prepare the dataframe that we will pass to `model.predict()`. This will contain some portion of time series during the training period so we can create any features that require historic data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d80416ff-ee17-4e88-a715-86f2a49fb40e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- CREATE DYNAMIC PREDICTION DATAFRAME  --- #\n",
    "# We will recursively append our forecasts to this \n",
    "# dataframe and re-compute our lag and window features from the\n",
    "# target in this dataframe. It contains data in both the training period \n",
    "# and forecast period which is needed for some transformers (e.g., lags and windows).\n",
    "look_back_start_time = forecast_start_time - look_back_window_size\n",
    "\n",
    "# Create `df_predict` which has data going as far back\n",
    "# as needed to create features which need past values.\n",
    "df_predict = df_train.loc[look_back_start_time:].copy()\n",
    "\n",
    "# Extend index into forecast horizon\n",
    "df_predict = pd.concat([\n",
    "                  df_predict,\n",
    "                  pd.DataFrame(index=forecast_horizon)\n",
    "                 ])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65945f51-985d-46e3-a137-21cd2965312e",
   "metadata": {},
   "source": [
    "Let's recursively create `X_test` and make our predictions and append them to the `df_predict` dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d629a47f-6f84-4dea-8a53-4e7c4d1fae9c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# --- RECURSIVE FORECASTING LOOP --- #\n",
    "for forecast_time in forecast_horizon:    \n",
    "    # Compute features during the forecast horizon\n",
    "    X_test = pipeline.transform(df_predict)\n",
    "    X_test = X_test.loc[[forecast_time]] \n",
    "\n",
    "    # Predict one step ahead. \n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    # Append forecast to the target variable columnn in our\n",
    "    # dynamic forecast dataframe `df_predict`. This `df_predict`\n",
    "    # is ready for the next iteration where we will re-compute\n",
    "    # features derived from the target such as lags and windows.\n",
    "    df_predict.loc[[forecast_time], target] = y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45b746cd-135f-46b4-8929-4b7e9ecd391d",
   "metadata": {},
   "source": [
    "Let's retrieve our forecast and actuals during the forest horizon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7931aa06-0468-41dc-b8a0-43dca249500d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- GET FORECAST AND TEST VALUES --- #    \n",
    "y_forecast = df_predict.loc[forecast_horizon, target]\n",
    "y_test = df_test.loc[forecast_start_time:, target]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8c65617-d15a-48ad-b326-faa79c51b2f8",
   "metadata": {},
   "source": [
    "Let's create predictions on the training set using our one step ahead forecast model. This is useful to plot when debugging models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3b511c05-ce4e-449d-b849-2ab556d681d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- CREATE IN-SAMPLE PREDICTIONS--- #\n",
    "y_forecast_train = model.predict(X_train)\n",
    "y_forecast_train = pd.DataFrame(y_forecast_train, index=X_train.index, columns=target)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86ba2671-7108-4a0d-a696-ba88fc844cf1",
   "metadata": {},
   "source": [
    "Let's plot the forecast!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ca0a3741-e4ec-4811-9e64-ce682b83a30f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Recursive forecast with LinearRegression()')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- PLOTTING --- #\n",
    "# Plot the forecast.\n",
    "fig, ax = plt.subplots(figsize=[12, 7])\n",
    "\n",
    "# Plot training set.\n",
    "y_train.plot(ax=ax, marker='.')\n",
    "# Plot actuals in forecasting horizon.\n",
    "y_test.plot(ax=ax, marker='.', alpha=0.6)\n",
    "# Plot forecast.\n",
    "y_forecast.plot(ax=ax)\n",
    "# Plot 1 step forecasts in training data.\n",
    "y_forecast_train.plot(ax=ax)\n",
    "\n",
    "ax.legend([\"train\", \"test\", \"forecast\", \"in-sample forecast\"])\n",
    "ax.set_xlim(xmin=y_train.index.max() - pd.DateOffset(weeks=3))\n",
    "ax.set_xlabel(\"Time\")\n",
    "ax.set_ylabel(f\"{target}\")\n",
    "ax.set_title(f\"Recursive forecast with {model}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b329343e-5055-4a8a-903d-4ec0252239a4",
   "metadata": {},
   "source": [
    "Let's compute the RMSE of this forecast."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ca4d4411-a247-47cd-882c-bd67833b69f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1210.143113549304"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute error metrics.\n",
    "from sklearn.metrics import mean_squared_error\n",
    "mean_squared_error(y_true=y_test.loc[y_forecast.index],\n",
    "                   y_pred=y_forecast,\n",
    "                   squared=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d649f5f-cfbb-43b8-83d1-b168514acb9b",
   "metadata": {},
   "source": [
    "Feel free to change the dates, try different models, and different features!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "705f9886-3682-40b9-a828-fe0dedafc232",
   "metadata": {},
   "source": [
    "We can see how seasonal dummy features can capture seasonality. In particular they can help with linear models as it enables them to learn a separate parameter associated with each seasonal index (e.g., each month of the year), tree-based models are less likely to benefit from these dummy features because they can split on the numeric form of the datetime feature to achieve the same effect."
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